Orchestration of learning and execution of model predictive control tool for manufacturing processes

ABSTRACT

Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

FIELD

The present application relates generally to computers and computerapplications, and more particularly to learning and execution of modelpredictive control for a manufacturing process.

BACKGROUND

In manufacturing, data-driven predictive models may be generated to makeprocess control decisions from real-time IoT (Internet of Things) data.IoT is a technological foundation for connectivity and messaging ofsensor data from sensors, devices, equipment and unit operations(stages) in homes, buildings, factories, manufacturing productionprocesses, cars and other connected objects.

BRIEF SUMMARY

A system and method of controlling a manufacturing process may beprovided. The system, in one aspect, may include at least one firsthardware processor. At least one first hardware processor may becommunicatively coupled to a control system that actuates actions tocontrol the manufacturing process. At least one first hardware processormay be operable to receive manufacturing process characteristicsassociated with the manufacturing process. At least one first hardwareprocessor may be operable to, based on at least one of the manufacturingprocess characteristics, determine a prediction time at which to executea selected machine learning model selected from multiple trained machinelearning models, and at the prediction time, execute the selectedmachine learning model based on process data associated with themanufacturing process. Executing of the selected machine learning modelpredicts a control set point for future values of state variables of themanufacturing process. The control set point may be sent to the controlsystem to control the manufacturing process by adjusting to the controlset point.

A method of controlling a manufacturing process, in one aspect, mayinclude receiving manufacturing process characteristics associated withthe manufacturing process. The method may also include, based on atleast one of the manufacturing process characteristics, determining aprediction time at which to execute a selected machine learning modelselected from multiple trained machine learning models, and at theprediction time, executing the selected machine learning model based onprocess data associated with the manufacturing process, the executing ofthe selected machine learning model predicting a control set point forfuture values of state variables of the manufacturing process. Themethod may also include sending the control set point to a controlsystem to control the manufacturing process by adjusting to the controlset point.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating system components in one embodiment.

FIG. 2 is a diagram illustrating learning and prediction orchestrationfor controlling a manufacturing process in one embodiment.

FIG. 3 is another diagram illustrating system components in oneembodiment.

FIG. 4 is a flow diagram illustrating a method of orchestrating learningand predicting for controlling a manufacturing process in oneembodiment.

FIG. 5 illustrates a schematic of an example computer or processingsystem that may implement a system in one embodiment of the presentdisclosure.

DETAILED DESCRIPTION

A system, method and techniques are disclosed for an intelligent plantadvisory system that may adaptively generate predictive models andexecute an appropriate predictive model in real time to predict futurestate of the process and compute a control set point for controlling amanufacturing process. In one embodiment, a system and method may ingestmanufacturing process data in real time and continuously learn viaorchestration a causal relationship between a time series of processvariables and states of the manufacturing process as needed,continuously predict future states of the manufacturing process andcompute control set point via orchestration. An example of themanufacturing process includes a blast furnace operation in steelmanufacturing process.

In one embodiment, a model training and retraining are performed basedon one or more manufacturing characteristics. Examples of manufacturingcharacteristics include, but are not limited to, one or moremanufacturing process conditions such as high production rate and batchsize, a raw material grade, a product grade, one or more qualitymeasurement events such as measurement frequencies, one or more targetvariable types such as a temperature of a product exiting from theprocess and a chemical composition of a product, and other manufacturingprocess characteristics.

In one embodiment, a model predictive control (MPC) system architectureand apparatus for orchestrating continuous learning (training) andreal-time execution (prediction) of machine learning or deep learningprediction model using process characteristics data for manufacturingprocess are provided.

The system, method and techniques in one embodiment provide forimprovement in a manufacturing process, for instance, by generatingprocess control decisions with which the manufacturing process iscontrolled and/or actuated.

FIG. 1 is a diagram illustrating system components in one embodiment.The system components and functions may be implemented and executed onone or more hardware processors. Real-time process data 102 may includedata measured by sensors during a manufacturing operation, in real time,for example, blast furnace operation in steel manufacturing. Forinstance, sensors may be coupled to, or mounted on, the wall of theblast furnace, and may provide real time measurements of the conditionsof the blast furnace during operation, such as the temperature andpressure, other state variables in the manufacturing process,controllable or control variables such as a charge or dumping rate ofinput material, for example, iron ore and coke, flow rate of blast air,moisture content of blast air, oxygen enrichment amount of blast air,and flow rate of pulverized coal. In the figure, example notation y mayrepresent target variable(s), x may represent state variable(s) and umay represent control variable(s).

A windowing of data component 104 receives the real time process data102, and groups the real time data into a time window. For instance, agroup of data includes real time data from time t to time t-q, where qis a time interval. The real time data may include variables such astemperature, pressure at different locations in the manufacturingprocess or operation, composition of raw material entering themanufacturing process. Feature extraction component 106 selects a subsetof variables in the group of data that impact the states of themanufacturing operation. For example, feature extraction component 106may remove from the group variables that are not of interest, forinstance, temperature and variables from certain locations of themanufacturing process. Selection may be done based on one or more rulesor algorithms implemented based on expert knowledge. Feature derivationcomponent 108 derives additional variables related to the real time datain the group of data. Feature derivation may be achieved based on one ormore rules or algorithms implemented based on expert knowledge. Anexample feature that is derived may include, but is not limited to, astandard deviation of a subset of the variables. Outlier removalcomponent 110 removes data that is determined to be an outlier. Forinstance, variable data that is outside of a 2 or 3 standard deviationmay be removed.

Data preprocessing for data window component 112 may preprocess thegroup of real time data, for example, determine the average value ofeach variable in the group, the mean of each variable in the group, andthe variance of each variable in the group.

The pre-processed data at 112 is sent to a storage device storing adatabase of historian data 114, for example, via a message broker 116,which communicates data between processing components. The historiandata database 114 stores past process data, for example, pre-processeddata.

Learning (or training) orchestrator component 118 determines the time totrain a machine learning model based on the pre-processed data stored inthe historian data database 114. For example, the learning orchestrator118 may periodically inspect the historian data 114 and responsive torecognizing a pattern in the historian data 114, triggers learning ortraining of a machine learning model. Pattern recognition may be basedon one or more rules or algorithms. A rule may specify to inspect thedata from the current time to a specified past time, and determinewhether a criterion is met. For instance, the learning orchestrator 118determines a learning frequency or time for a variable, for each ofdifferent variables. For example, the learning orchestrator 118 maydetermine that a model that predicts variable y should be run every 2hour. As another example, the learning orchestrator 118 may determinethat a model that predicts variable y should be run at 9 o'clock. At thedetermined frequency or time, the learning orchestrator 118 trains amodel, generating a trained machine learning model 120. In this way,multiple trained machine learning models are generated.

Model publisher component 122 generates or updates a catalogue thatincludes a list of trained machine learning models and their attributes.The catalog is sent to a model subscriber component 124, for instance,via the message broker 116. The model subscriber component 124 receivesand holds the catalogue of models.

Based on the catalogue, prediction orchestrator component 126 selects atrained machine learning model to execute, and also determines when(e.g., the frequency or time) to execute the selected trained machinelearning model, and/or when to execute an optimization of the selectedtrained machine learning model. Based on the determined time orfrequency, the trained machine learning model may be executed (e.g.,128), and/or optimized (e.g., 130).

Executing and/or optimizing of the selected trained machine learningmodel determines or predicts control set point or points, which may besent to a control system for controlling the manufacturing process.

FIG. 2 illustrates learning and prediction orchestration for controllinga manufacturing process in one embodiment. Manufacturing process data202 (e.g., data associated with manufacturing operation in producing aproduct) may be received from one or more sensors 204 and stored, forexample, on a memory or storage device. Manufacturing process data 202may include data indicative of manufacturing process characteristics.Manufacturing process characteristics may include data associated withtarget variable type 206, processing condition 208, raw material grade210, product grade 212, quality measurement event 214 and one or moreother process characteristics 216.

One or more hardware processors may be coupled to one or more storagedevices and perform learning orchestration 218. For example, thelearning orchestration 218 includes, based on at least one of themanufacturing process characteristics (e.g., 206, 208, 210, 212, 214,216), determining a learning time at which to train a machine learningmodel, and at the learning time, training the machine learning modelbased on historical process data associated with the manufacturingprocess. In one embodiment, the learning time at which to train amachine learning model is determined based on one or more trainingorchestration rules or criteria 220, for example, stored on a storagedevice. Based on at least one of the manufacturing processcharacteristics meeting a criterion or a rule, the rule may be triggeredto train a machine learning model at a specified time. An example of thetraining orchestration rules may be: If a high grade product is producedwith raw materials from a mine in X geographic location, the training ofSilicone content prediction model has to be every 1000 tons produced.Another example may be: If a measurement interval of hot metaltemperature (HMT) in a blast furnace is irregular, train the HMTprediction model N minutes (e.g., 5 minutes) after each measurement ofhot metal temperature. The trained machine learning model 222 is storedon a storage or memory device. Model attributes 224 of the trainedmachine learning model 22 are also extracted and stored, for example, aspart of a catalogue of models. The model attributes describe a trainedmachine learning model. For example, the model attributes may includethe response variable, time of training, time taken for the training,production line number, process number, product grade, raw materialgrades, and/or other attributes.

The manufacturing process continues and one or more hardware processorscontinues to receive additional (e.g., new) manufacturing processcharacteristics (e.g., 206, 208, 210, 212, 214, 216). Based on theadditional or new manufacturing process characteristics received, one ormore hardware processors repeats determining of the learning time andtraining of the machine learning model at the learning time. In thisway, multiple trained machine learning models 222 are generated andstored on a storage or memory device.

One or more hardware processors performing prediction orchestration 226receives the manufacturing process characteristics (e.g., 206, 208, 210,212, 214 and 216), for example, in real time, and determines aprediction time at which to execute a selected machine learning modelselected from the multiple trained machine learning models 222, and atthe prediction time, executes the selected machine learning model basedon process data associated with the manufacturing process. Theprediction time may be determined based on at least one of themanufacturing process characteristics meeting a rule condition orcriterion. For example, in one embodiment, the prediction time at whichto execute the selected machine learning model is determined based onone or more prediction orchestration rules or criteria 226, for example,stored on a storage device. Based on at least one of the manufacturingprocess characteristics meeting a criterion or a rule, the rule may betriggered to execute a selected machine learning model at a specifiedtime. The prediction orchestration rule database 226 may also storerules for selecting a machine learning model to execute. Based on atleast one of the received manufacturing characteristics (e.g., real-timedata), a trained machine learning model may be selected from a pluralityof trained models (e.g., 222). An example of a rule may be: Forpredicting hot metal temperature of blast furnace, if the current seasonis summer time, select a blast furnace hot metal temperature predictionmodel that was trained during the summer time, execute the predictionmodel every 20 minutes for the next 1 hour into the future. Anotherexample of a rule may be: If the current manufacturing process isproducing a low grade product, select a prediction model that wastrained during the manufacturing process when low grade product wasproduced, and execute the prediction model every 2 hours.

The executing of the selected machine learning model predicts a controlset point or a set of control set points for future values of statevariables in the manufacturing process, for example, as shown at 228 and210. For instance, a selected machine learning model may be a predictionmodel 228. Another example of a selected machine learning model may bean optimization model 230. One or more hardware processors may send thecontrol set point or the set of control set points to a control systemto control the manufacturing process by automatically adjusting theparameters of the manufacturing processor according to the control setpoint.

One or more hardware processors orchestrating the predicting may bereferred to as one or more first hardware processors. One or morehardware processors orchestrating the learning may be referred to as oneor more second hardware processors. The manufacturing process may be acontinuous manufacturing process, for example, a continuous blastfurnace operation in steel manufacturing. In one embodiment, themanufacturing process characteristics are received in real time. Acatalogue of the multiple trained machine learning models may becommunicated to at least one first hardware processor, and at least onefirst hardware processor may pull a set of the multiple trained machinelearning models from the storage device.

FIG. 3 is another diagram illustrating system components in oneembodiment. A modeling orchestrator 302 may include one or more hardwareprocessors that determine a time for training (or retraining) a model.One or more hardware processors may pull or retrieve a set of processhistorian data 304 (e.g., stored on a storage device or memory device)to train a model, and at the determined time, may perform machinelearning training 306. The machine learning training 306 generates atrained model 308. Information associated with the trained model 308 ispublished as shown at 310. The modeling orchestrator 302 repeats theprocess of determining the time, and training a model, based on realtime received process data, creating multiple trained (or learned)models 308. Each of the trained or newly trained models may be stored ona storage device, and may be catalogued with associated attributes. Forinstance, one or more hardware processors may perform a model publisherprocess or function 310, publishing a catalogue or a list of the trainedmodels with associated attributes.

The model publisher process 310 (e.g., a hardware processor executingthe model publisher function) communicates the catalogue comprising thelist of the trained models with associated attributes to a messagebroker function 312 (e.g., a hardware processor executing a messagebroker function). The communication of the catalogue may be performedperiodically and/or responsive to an update to the catalogue.

The message broker function 312 (e.g., a hardware processor executing amessage broker function) sends the catalogue of the list of trainedmachine learning models to a model subscriber function 314 (e.g., ahardware processor executing a model subscriber function). The messagebroker function 312 also receives data associated with a manufacturingprocess 316, e.g., data resulting from streams operation of dataingestion 318, 320 and pre-processing of real-time processed data 322 toa process historian database 304.

A manufacturing process 316 may have sensors 338 coupled to the processthat measure or sense process data associated with the manufacturingprocess. Programmable logic controller (PLC), Data Acquisition system(DAS), and/or sensors may collect process data during the manufacturingproduct operation 316. A gateway 336, for example, one or more hardwareprocessors, receives the data (e.g., measured data) in real time andstores the real-time process data 334 on a storage device. For example,a blast furnace in steel manufacturing may include a number of sensors(e.g., temperature sensor, pressure sensor) mounted on the wall of theblast furnace. The sensors measure blast furnace operation data (e.g.,operating temperature and pressure) in real time, which are stored in adatabase.

A runtime orchestrator 324 may include one or more hardware processorsthat perform runtime orchestration functions or processes. For example,a model subscriber function 314 pulls or receives newly trained machinelearning models via the message broker 312 for performing real-timeprediction (e.g., 326, 328) and control action computation 330. Ahardware processor performing a runtime orchestrator function 324 maydetermine a time for new prediction and/or optimization, select atrained machine learning model, and make the new prediction (e.g.,future values of state variables and control set point(s)) by executingthe selected trained machine learning model, for example, as shown at326 and 328. For instance, a prediction model may be selected andexecuted as shown at 326, at the determined time. As another example, anoptimization model may be selected and executed as shown at 328, at thedetermined time.

Prediction 326 and optimization 328 are performed based on the real-timeprocess data 334 that is ingested at 318 and 320, and pre-processed at322. Data initialization starts after a production is in full scale(e.g., production is stabilized after a restart) so that data areingested for model learning and prediction. For example, for a process,production may be stabilized 10 hour after a start up. Data ingestionrefers to a continuous pulling of data from the data repository ofreal-time process data (320) in determined frequency. In one embodiment,determining of the prediction time and selecting of a trained model toexecute are performed based on one or more manufacturing processcharacteristics retrieved from the pre-processed data 322. In oneembodiment, determining the time to train a model (e.g., in modelingorchestrator 302) is performed based on one or more manufacturingprocess characteristics retrieved from the pre-processed data 322received via the message broker 312.

The result of the prediction or optimization includes one or morecontrol set points, which are sent to a control system 332. The controlsystem 332 actuates or controls a manufacturing process 316 byperforming control action(s) or adjusting the manufacturing process 316according to the result of the prediction. Performing control action(s)or adjusting the manufacturing process 316 according to the result ofthe prediction in turn updates processing conditions of themanufacturing process 316, which in turn produces new process data.

In one embodiment, the modeling orchestration may be performed oncloud-based learning system, for example, off-line. In one embodiment,the runtime orchestration may be performed on premise, for example,in-line.

FIG. 4 is a flow diagram illustrating a method in one embodiment. Themethod may be performed or executed by one or more hardware or computerprocessors. At 402, manufacturing process characteristics associatedwith a manufacturing process are received. In one embodiment, themanufacturing process characteristics are received in real time.Examples of the manufacturing process characteristics may include, butare not limited to, a processing condition, a raw material grade, aproduct grade, and a quality measurement event. The manufacturingprocess may be a continuous manufacturing process. An example of themanufacturing process includes a blast furnace operation in steelmanufacturing.

At 404, based on at least one of the manufacturing processcharacteristics, a learning time at which to train a machine learningmodel is learned, and at the learning time, the machine learning modelis trained based on historical process data associated with themanufacturing process. The learning time may be determined based on theat least one of the manufacturing process characteristics meeting atleast one criterion or a rule. The manufacturing process continues andadditional or new manufacturing process characteristics are received,and determining of the learning time and training of the machinelearning model repeat based on the additional or new manufacturingprocess characteristics. In this way, multiple trained machine learningmodels are generated and stored on a storage device.

At 406, based on at least one of the manufacturing processcharacteristics, a prediction time at which to execute a selectedmachine learning model selected from multiple trained machine learningmodels is determined, and at the prediction time, the selected machinelearning model is executed based on process data associated with themanufacturing process. The execution or prediction time may bedetermined based on the at least one of the manufacturing processcharacteristics meeting at least one criterion or a rule. One or morerules may also be run to determine the selected machine learning model,for example, to select a machine learning model from a plurality oftrained machine learning models. In one embodiment, one or more rulesmay simultaneously determine which of the trained machine learningmodels to select and at what time to execute the selected machinelearning model. Execution of the selected machine learning modelpredicts a control set point for future values of state variables of themanufacturing process.

At 408 the control set point to a control system to control themanufacturing process by adjusting to the control set point. Forinstance, the control system may autonomously actuate an action oradjust a manufacturing process parameter to control the manufacturingprocess in real-time based on the real-time based model prediction.

FIG. 5 illustrates a schematic of an example computer or processingsystem that may implement a system in one embodiment of the presentdisclosure. The computer system is only one example of a suitableprocessing system and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the methodologydescribed herein. The processing system shown may be operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with the processing system shown in FIG. 5 may include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a module 30 that performsthe methods described herein. The module 30 may be programmed into theintegrated circuits of the processor 12, or loaded from memory 16,storage device 18, or network 24 or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

We claim:
 1. A system controlling a manufacturing process, comprising:at least one first hardware processor; the least one first hardwareprocessor communicatively coupled to a control system that actuatesactions to control the manufacturing process, and operable to perform:receiving manufacturing process characteristics associated with themanufacturing process; based on at least one of the manufacturingprocess characteristics, determining a prediction time at which toexecute a selected machine learning model selected from multiple trainedmachine learning models, and at the prediction time, executing theselected machine learning model based on process data associated withthe manufacturing process, the executing of the selected machinelearning model predicting a control set point for future values of statevariables of the manufacturing process; and sending the control setpoint to the control system to control the manufacturing process byadjusting to the control set point.
 2. The system of claim 1, furthercomprising: at least one storage device; at least one second hardwareprocessor coupled to the at least one storage device, the at least onesecond hardware processor operable to perform: based on at least one ofthe manufacturing process characteristics, determining a learning timeat which to train a machine learning model, and at the learning time,training the machine learning model based on historical process dataassociated with the manufacturing process, wherein as the manufacturingprocess continues and additional manufacturing process characteristicsare received, the determining of the learning time and the training ofthe machine learning model repeat, wherein the multiple trained machinelearning models are generated and stored on the at least one storagedevice.
 3. The system of claim 2, wherein the learning time isdetermined based on the at least one of the manufacturing processcharacteristics meeting at least one criterion.
 4. The system of claim2, wherein the manufacturing process is a continuous manufacturingprocess.
 5. The system of claim 2, wherein the manufacturing processincludes a blast furnace operation in steel manufacturing.
 6. The systemof claim 2, wherein the manufacturing process characteristics comprise aprocessing condition, a raw material grade, a product grade, and aquality measurement event.
 7. The system of claim 2, wherein themanufacturing process characteristics are received in real time.
 8. Thesystem of claim 2, wherein a catalogue of the multiple trained machinelearning models are communicated to the at least one first hardwareprocessor, and the at least one first hardware processor is operable topull a set of the multiple trained machine learning models from thestorage device.
 9. The system of claim 2, wherein the prediction time isdetermined based on the at least one of the manufacturing processcharacteristics meeting at least one criterion.
 10. A computer readablestorage medium storing a program of instructions executable by a machineto perform a method of controlling a manufacturing process, the methodcomprising: receiving manufacturing process characteristics associatedwith the manufacturing process; based on at least one of themanufacturing process characteristics, determining a prediction time atwhich to execute a selected machine learning model selected frommultiple trained machine learning models, and at the prediction time,executing the selected machine learning model based on process dataassociated with the manufacturing process, the executing of the selectedmachine learning model predicting a control set point for future valuesof state variables of the manufacturing process; and sending the controlset point to a control system to control the manufacturing process byadjusting to the control set point.
 11. The computer readable storagemedium of claim 10, further comprising: based on at least one of themanufacturing process characteristics, determining a learning time atwhich to train a machine learning model, and at the learning time,training the machine learning model based on historical process dataassociated with the manufacturing process, wherein as the manufacturingprocess continues and additional manufacturing process characteristicsare received, the determining of the learning time and the training ofthe machine learning model repeat, wherein the multiple trained machinelearning models are generated and stored on a storage device.
 12. Thecomputer readable storage medium of claim 11, wherein the manufacturingprocess characteristics are received in real time from one or moresensors mounted on manufacturing equipment associated with themanufacturing process.
 13. The computer readable storage medium of claim11, wherein the manufacturing process characteristics comprise aprocessing condition, a raw material grade, a product grade, and aquality measurement event.